GPT-5.3 Codex vs Claude Opus 4.6: Full Comparison
February 07, 2026 · 5 min read
GPT-5.3 Codex and Claude Opus 4.6 both dropped on February 5, 2026 — within fifteen minutes of each other. Two frontier models, two very different strategies, one shared message: AI coding agents have arrived. Here’s how they compare, what each does best, and what these cloud giants mean for anyone who cares about running AI locally.
What Is GPT-5.3 Codex?
GPT-5.3 Codex is OpenAI’s latest coding-focused model, built by merging their Codex and GPT-5 training stacks. It’s not a general-purpose GPT-5.3 — GPT-5.2 remains their flagship general model. Instead, this is a specialist designed for the full software development lifecycle.
Key capabilities:
- 25% faster than GPT-5.2 Codex
- State-of-the-art on SWE-Bench Pro and Terminal-Bench 2.0 benchmarks
- Handles long-running agentic tasks: debugging, deploying, monitoring, writing documentation, and testing
- Builds complex web applications and games autonomously over extended sessions
- Supports a 400K token context window
- Users can steer the model mid-task with real-time status updates
Perhaps the most striking detail: GPT-5.3 Codex was instrumental in creating itself. OpenAI’s team used early versions to debug training, manage deployments, and diagnose test results during development.
Availability: Command line, IDE extension, web interface, and a new macOS desktop app. API access is rolling out.
What Is Claude Opus 4.6?
Claude Opus 4.6 is Anthropic’s most intelligent model to date, succeeding Opus 4.5 with substantial improvements across reasoning, coding, and agentic work. Unlike GPT-5.3 Codex, Opus 4.6 is a general-purpose model — not just a coding specialist.
Key capabilities:
- 1 million token context window (in beta) — the first Opus-class model with this capacity
- Adaptive thinking: automatically adjusts reasoning depth based on task complexity, replacing manual extended thinking toggles
- Leads all frontier models on Humanity’s Last Exam (complex multidisciplinary reasoning)
- Top score on Terminal-Bench 2.0 (agentic coding)
- Outperforms GPT-5.2 by approximately 144 Elo points on GDPval-AA (economically valuable knowledge work tasks)
- Enhanced financial analysis, research, and document processing
The security story is remarkable. During testing, Opus 4.6 discovered over 500 previously unknown zero-day vulnerabilities in open-source code — each validated by Anthropic researchers or external security experts. It used standard security tools like debuggers and fuzzers, but without any specialized instructions. Anthropic’s head of frontier red team said it could become “the main way in which open-source software moving forward was secured.”
Availability: Claude Pro, Max, Team, and Enterprise. Also on Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry.
Head-to-Head Comparison
| GPT-5.3 Codex | Claude Opus 4.6 | |
|---|---|---|
| Focus | Coding specialist | General-purpose + coding |
| Context window | 400K tokens | 1M tokens (beta) |
| Terminal-Bench 2.0 | State-of-the-art | Highest score |
| SWE-Bench Pro | State-of-the-art | Strong |
| Humanity’s Last Exam | Not reported | Leads all frontier models |
| GDPval-AA | Not reported | +144 Elo over GPT-5.2 |
| Reasoning mode | Standard | Adaptive thinking |
| Security research | Not highlighted | 500+ zero-days found |
| Self-building | Yes — helped create itself | Not reported |
| API available | Rolling out | Yes |
Bottom line: Opus 4.6 appears to have the edge for complex reasoning, long-context work, and general knowledge tasks. GPT-5.3 Codex is laser-focused on software engineering workflows and excels at sustained agentic coding sessions. Preliminary impressions from developers with access to both models suggest they’re closely matched on coding, but Opus 4.6 pulls ahead on breadth.
Pricing Comparison
| GPT-5.3 Codex | Claude Opus 4.6 | |
|---|---|---|
| Input | $1.25 / 1M tokens | $5.00 / 1M tokens |
| Output | $10.00 / 1M tokens | $25.00 / 1M tokens |
| Cached input | $0.125 / 1M tokens | Not published |
GPT-5.3 Codex is significantly cheaper per token. For coding-heavy workflows with large output volumes, the cost difference adds up fast. Opus 4.6 commands a premium, but brings a broader capability set and a larger context window.
What Does This Mean for Local AI?
Every frontier cloud release like this is worth celebrating — and worth questioning. These models are genuinely impressive. They can build software, find security vulnerabilities, and reason through complex problems at a level that was science fiction five years ago. But they come with trade-offs that matter.
The privacy equation hasn’t changed
Both models process your data on remote servers. Every prompt, every code snippet, every document you paste into the context window leaves your device and lands on infrastructure you don’t control. For personal projects, that might be fine. For proprietary code, client data, medical records, or legal documents, it’s a real concern — especially with the EU AI Act reaching full enforcement in August 2026.
The cost equation is shifting
At $10–25 per million output tokens, heavy usage adds up. Meanwhile, local models run at zero marginal cost after the initial setup. A quantized 14B model on a Mac with 32GB of RAM handles writing, summarization, code assistance, and brainstorming without sending a single byte to the cloud — and without a monthly bill. For a deeper look at the math, see our local AI vs cloud cost comparison.
Local models are closing the gap
The gap between frontier cloud models and the best local alternatives is real, but it’s narrowing. Qwen 3’s mobile variants, Gemma 3, and quantized versions of larger open-source models are remarkably capable for everyday tasks. Not every task needs a 1-million-token context window or state-of-the-art SWE-Bench scores. For the 80% of interactions that involve writing, brainstorming, summarizing, and light coding, a well-chosen local model on your own device gets the job done — privately and for free.
The best setup might be both
There’s no reason to choose only one approach. Use cloud models like GPT-5.3 Codex or Opus 4.6 for the complex agentic tasks they excel at — multi-file refactors, security audits, frontier reasoning. Use a local model for everything else: daily writing, private brainstorming, sensitive documents, offline work, and any task where privacy outweighs peak performance.
This hybrid approach — cloud when you need cutting-edge capability, local when you need privacy and independence — is exactly what tools like Enclave AI are built for.
Looking Ahead
The simultaneous release of GPT-5.3 Codex and Claude Opus 4.6 shows the cloud AI race is intensifying. But every frontier advance also trickles down. The research techniques, training methods, and architectural innovations in these models eventually make their way into open-source alternatives that run on your hardware.
The question isn’t whether cloud or local AI will win. It’s how quickly the local ecosystem can absorb what the frontier discovers — and for a growing number of tasks, it already has.